Prevalence of oral submucous fibrosis among areca nut chewers: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Worldwide millions peoples consume AN who are at risk of OSMF. Prevalence of OSMF is reported between 0.03% and 30% irrespective of AN habit. Further, these estimates are based on sample population comprised of OSMF patients or general population rather AN chewers (ANC). Therefore, available evidence does not reflect the true prevalence of OSMF among ANC. METHOD: The studies providing the prevalence of OSMF in ANC were identified in PubMed, Scopus, and Web of Science. Pooled prevalence and quality assessment using New-Ottawa Scale were performed. RESULTS: Fifteen studies reported the prevalence of OSMF (929) in ANC (53,213). Most studies were from China (six studies) and India (four studies) correlating with regions having high ANC. The pooled prevalence of OSMF in ANC was 5% (0.05 [95% CI, 0.03, 0.08]). All studies' quality was satisfactory; however, the OSMF diagnosis method, age, gender, and habits need further scrutiny. CONCLUSION: Available evidence suggested a low prevalence of OSMF in ANC, although further large-scale studies are recommended to validate this finding. Understanding the prevalence and distribution patterns of OSMF might aid intervention healthcare programs and contribute to the reduction of the oral cancer burden related to OSMF.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".